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NVIDIA Generative AI Multimodal Sample Questions:

1. You are deploying a Riva-based speech-to-text service in a production environment. You observe high latency and CPU utilization on your server Which of the following actions would be most effective in optimizing the performance of your Riva service?

A) Increasing the audio chunk size sent to the Riva server to reduce the number of requests.
B) Disabling automatic punctuation and capitalization to simplify the ASR process.
C) Enabling batching and concurrency in the Riva server configuration to process multiple requests simultaneously.
D) Switching to a smaller, less accurate ASR model to reduce computational load.
E) Deploying the Riva server on a CPU-only instance to reduce cost.

2. You are developing a virtual assistant using NVIDIAACE. You want to ensure that the avatar's facial expressions and lip movements are synchronized with the generated speech in real-time. Which NVIDIA SDKs and ACE components are essential for achieving this?

A) Triton Inference Server for deploying all AI models, Riva for voice cloning, and Omniverse for character creation.
B) NeMo for text-to-speech, Audio2Face for generating blendshape weights, and a real-time rendering engine (e.g., Unity or Unreal Engine) to drive the avatar.
C) Riva for speech synthesis, NeMo for language modeling, and Audi02Face for animation.
D) CUDA for GPU acceleration, TensorRT for model optimization, and Audi02Emotion for expression generation.
E) Riva for speech recognition, Triton Inference Server for model deployment, and Omniverse for 3D rendering.

3. Consider a scenario where you are building a multimodal model to generate realistic indoor scenes. You have access to text descriptions of the scene, 3D models of furniture, and ambient sound recordings. Which of the following loss functions would be most appropriate to ensure coherence and realism in the generated scenes?

A) Cosine similarity loss between the generated image and the input 3D models.
B) KL Divergence loss between the generated sound and the input text.
C) Cross-entropy loss for classifying different object categories in the scene.
D) A combination of adversarial loss (GAN) to ensure realism, a perceptual loss to match high-level features, and a semantic consistency loss to align the generated image with the input text description.
E) Mean Squared Error (MSE) between the generated image and a reference image.

4. You're building a text generation model using a Transformer architecture. You observe that the generated text often gets stuck in repetitive loops, producing the same phrase over and over. Which of the following strategies is MOST likely to mitigate this issue?

A) Implement beam search with a larger beam width.
B) Increase the number of attention heads in the Transformer.
C) Increase the temperature parameter during text generation.
D) Use a smaller vccabulary size.
E) Decrease the learning rate of the model during training.

5. You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?

A) Increase the learning rate.
B) Implement early stopping based on the validation loss.
C) Increase the weight decay (L2 regularization).
D) Decrease the learning rate.
E) Increase the training data size by collecting more data.

Solutions: